{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IzVsNhi1jbAE"
      },
      "source": [
        "# Supporting analysis for manuscript *“Then electricity theft would end, nobody loves stealing”: Community-based solutions for improving electricity access in informal settlements in Kampala, Uganda*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BGTrDoWpjo13"
      },
      "source": [
        "# Import libraries, data and pre-analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mYLw3OmCjxl-"
      },
      "source": [
        "### Import necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "axMz6SILj2GQ"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "from cycler import cycler\n",
        "import numpy as np\n",
        "import string\n",
        "import matplotlib.patches as mpatches\n",
        "import matplotlib as mpl\n",
        "import seaborn as sns\n",
        "from google.colab import drive\n",
        "import matplotlib.ticker as mtick\n",
        "from matplotlib.ticker import PercentFormatter\n",
        "from IPython.display import display\n",
        "import re\n",
        "from  matplotlib.colors import LinearSegmentedColormap, to_rgba, rgb2hex\n",
        "import plotly.graph_objects as go\n",
        "import plotly.express as px\n",
        "import plotly.colors\n",
        "from matplotlib.lines import Line2D\n",
        "from matplotlib.ticker import FuncFormatter\n",
        "from scipy.interpolate import make_interp_spline\n",
        "from scipy.ndimage import gaussian_filter1d"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sB_1rO3rj7AY"
      },
      "source": [
        "### Set global formatting parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "p1c5GRWWjeR8"
      },
      "outputs": [],
      "source": [
        "# Set the default text font size\n",
        "plt.rc('font', size=18)\n",
        "# Set the axes title font size\n",
        "plt.rc('axes', titlesize=18)\n",
        "# Set the font size for x tick labels\n",
        "plt.rc('xtick', labelsize=18)\n",
        "# Set the font size for y tick labels\n",
        "plt.rc('ytick', labelsize=18)\n",
        "# Set the legend font size\n",
        "plt.rc('legend', fontsize=18)\n",
        "# Set the font size of the figure title\n",
        "plt.rc('figure', titlesize=20)\n",
        "\n",
        "colors = ['#FAC710',\n",
        "          '#414BB2',\n",
        "          '#9510AC',\n",
        "          '#F24726',\n",
        "          '#12CDD4',\n",
        "          '#8FD14F',\n",
        "          '#696969',\n",
        "          '#FEF445']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8HItUeSTkC8Q"
      },
      "source": [
        "### Define file path and read in survey data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "aCj7A21w2YQR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "78d5547b-889d-4547-87f7-94dc4a771a87"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "5ET9zF1gkAan"
      },
      "outputs": [],
      "source": [
        "path = ('/content/drive/My Drive/Spotlight Kampala/Phase 1 Fieldwork/Surveys/')\n",
        "path1 = ('/content/drive/My Drive/Spotlight Kampala/Phase 1 Fieldwork/Community Forums/Data/')\n",
        "path2 = ('/content/drive/My Drive/Spotlight Kampala/Phase 1 Fieldwork/Remote Monitoring/')\n",
        "fig_path = ('/content/drive/My Drive/Spotlight Kampala/Outputs/Community-based solutions for improving electricity access in informal settlements in Kampala, Uganda/Graphics/')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(path + 'Spotlight Kampala Final Survey Dataset.csv')\n",
        "df1 = pd.read_csv(path1 + 'Community Forum Data Collection - Long-form raw data.csv')\n",
        "df2 = pd.read_csv(path2 + 'spotlight_kampala_volt_and_outs.csv')"
      ],
      "metadata": {
        "id": "yu6a_ZIF2WD9"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gS6UUGKlkx1n"
      },
      "source": [
        "### Uniformize connection type"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x6iLNQJKkiLt"
      },
      "outputs": [],
      "source": [
        "\n",
        "# Connection types differed by enumerator to protect the privacy of respondents\n",
        "# An analytical step is needed to uniformize them\n",
        "\n",
        "# Uniformize connection types\n",
        "enumerator = df['enumerator']\n",
        "connection_type = df['connection_type']\n",
        "\n",
        "corrected_modality = np.zeros((len(df['enumerator'])))\n",
        "\n",
        "enumerator_selections = [''] # Redacted to protect privacy of enumerators\n",
        "\n",
        "connection_options = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I']\n",
        "\n",
        "# 1 = Unelectrified\n",
        "# 2 = Individual metered connection\n",
        "# 3 = Individual unmetered connection\n",
        "# 4 = Individual dual connection\n",
        "# 5 = Collective metered connection\n",
        "# 6 = Collective unmetered connection\n",
        "# 7 = Collective dual connection\n",
        "\n",
        "for n in range(len(df['connection_type'])):\n",
        "    if df['enumerator'][n] == enumerator_selections[0]:\n",
        "        if df['connection_type'][n] == connection_options[0]: #A\n",
        "            corrected_modality[n] = 2\n",
        "        elif df['connection_type'][n] == connection_options[1]: #B\n",
        "            corrected_modality[n] = 3\n",
        "        elif df['connection_type'][n] == connection_options[2]: #C\n",
        "            corrected_modality[n] = 4\n",
        "        elif df['connection_type'][n] == connection_options[3]: #D\n",
        "            corrected_modality[n] = 1\n",
        "        elif df['connection_type'][n] == connection_options[4]: #E\n",
        "            corrected_modality[n] = 3\n",
        "        elif df['connection_type'][n] == connection_options[5]: #F\n",
        "            corrected_modality[n] = 6\n",
        "        elif df['connection_type'][n] == connection_options[6]: #G\n",
        "            corrected_modality[n] = 7\n",
        "        elif df['connection_type'][n] == connection_options[7]: #H\n",
        "            corrected_modality[n] = 5\n",
        "        elif df['connection_type'][n] == connection_options[8]: #I\n",
        "            corrected_modality[n] = 6\n",
        "\n",
        "    elif df['enumerator'][n] == enumerator_selections[1]:\n",
        "        if df['connection_type'][n] == connection_options[0]: #A\n",
        "            corrected_modality[n] = 2\n",
        "        elif df['connection_type'][n] == connection_options[1]: #B\n",
        "            corrected_modality[n] = 3\n",
        "        elif df['connection_type'][n] == connection_options[2]: #C\n",
        "            corrected_modality[n] = 4\n",
        "        elif df['connection_type'][n] == connection_options[3]: #D\n",
        "            corrected_modality[n] = 1\n",
        "        elif df['connection_type'][n] == connection_options[4]: #E\n",
        "            corrected_modality[n] = 3\n",
        "        elif df['connection_type'][n] == connection_options[5]: #F\n",
        "            corrected_modality[n] = 6\n",
        "        elif df['connection_type'][n] == connection_options[6]: #G\n",
        "            corrected_modality[n] = 7\n",
        "        elif df['connection_type'][n] == connection_options[7]: #H\n",
        "            corrected_modality[n] = 5\n",
        "        elif df['connection_type'][n] == connection_options[8]: #I\n",
        "            corrected_modality[n] = 6\n",
        "\n",
        "    elif df['enumerator'][n] == enumerator_selections[2]:\n",
        "        if df['connection_type'][n] == connection_options[0]: #A\n",
        "            corrected_modality[n] = 1\n",
        "        elif df['connection_type'][n] == connection_options[1]: #B\n",
        "            corrected_modality[n] = 6\n",
        "        elif df['connection_type'][n] == connection_options[2]: #C\n",
        "            corrected_modality[n] = 5\n",
        "        elif df['connection_type'][n] == connection_options[3]: #D\n",
        "            corrected_modality[n] = 2\n",
        "        elif df['connection_type'][n] == connection_options[4]: #E\n",
        "            corrected_modality[n] = 3\n",
        "        elif df['connection_type'][n] == connection_options[5]: #F\n",
        "            corrected_modality[n] = 6\n",
        "        elif df['connection_type'][n] == connection_options[6]: #G\n",
        "            corrected_modality[n] = 3\n",
        "        elif df['connection_type'][n] == connection_options[7]: #H\n",
        "            corrected_modality[n] = 7\n",
        "        elif df['connection_type'][n] == connection_options[8]: #I\n",
        "            corrected_modality[n] = 4\n",
        "\n",
        "    elif df['enumerator'][n] == enumerator_selections[3]:\n",
        "        if df['connection_type'][n] == connection_options[0]: #A\n",
        "            corrected_modality[n] = 7\n",
        "        elif df['connection_type'][n] == connection_options[1]: #B\n",
        "            corrected_modality[n] = 1\n",
        "        elif df['connection_type'][n] == connection_options[2]: #C\n",
        "            corrected_modality[n] = 3\n",
        "        elif df['connection_type'][n] == connection_options[3]: #D\n",
        "            corrected_modality[n] = 5\n",
        "        elif df['connection_type'][n] == connection_options[4]: #E\n",
        "            corrected_modality[n] = 6\n",
        "        elif df['connection_type'][n] == connection_options[5]: #F\n",
        "            corrected_modality[n] = 3\n",
        "        elif df['connection_type'][n] == connection_options[6]: #G\n",
        "            corrected_modality[n] = 4\n",
        "        elif df['connection_type'][n] == connection_options[7]: #H\n",
        "            corrected_modality[n] = 2\n",
        "        elif df['connection_type'][n] == connection_options[8]: #I\n",
        "            corrected_modality[n] = 6\n",
        "\n",
        "    elif df['enumerator'][n] == enumerator_selections[4]:\n",
        "        if df['connection_type'][n] == connection_options[0]: #A\n",
        "            corrected_modality[n] = 6\n",
        "        elif df['connection_type'][n] == connection_options[1]: #B\n",
        "            corrected_modality[n] = 7\n",
        "        elif df['connection_type'][n] == connection_options[2]: #C\n",
        "            corrected_modality[n] = 4\n",
        "        elif df['connection_type'][n] == connection_options[3]: #D\n",
        "            corrected_modality[n] = 3\n",
        "        elif df['connection_type'][n] == connection_options[4]: #E\n",
        "            corrected_modality[n] = 1\n",
        "        elif df['connection_type'][n] == connection_options[5]: #F\n",
        "            corrected_modality[n] = 5\n",
        "        elif df['connection_type'][n] == connection_options[6]: #G\n",
        "            corrected_modality[n] = 2\n",
        "        elif df['connection_type'][n] == connection_options[7]: #H\n",
        "            corrected_modality[n] = 3\n",
        "        elif df['connection_type'][n] == connection_options[8]: #I\n",
        "            corrected_modality[n] = 6\n",
        "\n",
        "    elif df['enumerator'][n] == enumerator_selections[5]:\n",
        "        if df['connection_type'][n] == connection_options[0]: #A\n",
        "            corrected_modality[n] = 5\n",
        "        elif df['connection_type'][n] == connection_options[1]: #B\n",
        "            corrected_modality[n] = 6\n",
        "        elif df['connection_type'][n] == connection_options[2]: #C\n",
        "            corrected_modality[n] = 1\n",
        "        elif df['connection_type'][n] == connection_options[3]: #D\n",
        "            corrected_modality[n] = 6\n",
        "        elif df['connection_type'][n] == connection_options[4]: #E\n",
        "            corrected_modality[n] = 2\n",
        "        elif df['connection_type'][n] == connection_options[5]: #F\n",
        "            corrected_modality[n] = 4\n",
        "        elif df['connection_type'][n] == connection_options[6]: #G\n",
        "            corrected_modality[n] = 3\n",
        "        elif df['connection_type'][n] == connection_options[7]: #H\n",
        "            corrected_modality[n] = 7\n",
        "        elif df['connection_type'][n] == connection_options[8]: #I\n",
        "            corrected_modality[n] = 3\n",
        "\n",
        "df['corrected_modality'] = corrected_modality.tolist()\n",
        "\n",
        "# Calculate total number of respondents with each connection type\n",
        "connection_count = np.zeros(7)\n",
        "\n",
        "for i in range(0,7):\n",
        "    connection_count[i] = len(df[(df['corrected_modality'] == (i+1))])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify the columns you want to print to the Excel file\n",
        "columns_to_print = ['corrected_modality', 'KEY']\n",
        "\n",
        "# Define the file path in your Google Drive\n",
        "excel_file_path = '/content/drive/MyDrive/Spotlight Kampala UCB/Shared Data/Spotlight Kampala Survey Dataset - Sharing UMass 12-5-2023.xlsx'\n",
        "\n",
        "# Use the to_excel method to write the selected columns to the Excel file\n",
        "df[columns_to_print].to_excel(excel_file_path, index=False)\n",
        "\n",
        "print(f'The selected columns have been written to {excel_file_path}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BPzhwAhMxNbG",
        "outputId": "e95598eb-e714-44bd-bd9d-4738a7595331"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The selected columns have been written to /content/drive/MyDrive/Spotlight Kampala UCB/Shared Data/Spotlight Kampala Survey Dataset - Sharing UMass 12-5-2023.xlsx\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_mmpHIGHlGjC"
      },
      "source": [
        "### Assign exchange rate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "38BymvSglIGa"
      },
      "outputs": [],
      "source": [
        "# Assign exchange rate\n",
        "exchange_rate = 3754.95 # Average exchange rate in November 2022 when the survey was completed https://www.investing.com/currencies/usd-ugx-historical-data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G5A2Ey_slJMU"
      },
      "source": [
        "### Calculate income quartiles"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "luxZ2oEulIwL"
      },
      "outputs": [],
      "source": [
        "income = df.loc[df['total_income'] > 0, 'total_income']\n",
        "income_np = income.to_numpy()\n",
        "percentiles = [0, 25, 50, 75, 100]\n",
        "percentile_values = np.percentile(income_np, percentiles)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7F9PZYDrwViH"
      },
      "source": [
        "### Calculate electricity burden"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q0mD2M1iwVQf"
      },
      "outputs": [],
      "source": [
        "df['Electricity_burden'] = df['elec_payment_amount']/df['total_income']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "average_electricity_burden = df['Electricity_burden'].mean(skipna=True)\n",
        "print(average_electricity_burden)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BBSbR7Wf3x0m",
        "outputId": "f0349eb2-481b-4e77-88a9-dc59d56bc371"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.05472038965138301\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Uniformize payment receipients"
      ],
      "metadata": {
        "id": "1wbBets8fzYK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Uniformize/clean payment recipients\n",
        "\n",
        "payment_options = ['umeme',\n",
        "                  'landlord',\n",
        "                  'neighbor',\n",
        "                  'kamyufu',\n",
        "                  'other',\n",
        "                  'umeme no_one',\n",
        "                  'landlord no_one',\n",
        "                  'umeme kamyufu',\n",
        "                  'landlord kamyufu',\n",
        "                  'kamyufu landlord',\n",
        "                  'no_one',\n",
        "                  'landlord neighbor',\n",
        "                  'kamyufu neighbor',\n",
        "                  'umeme landlord'] # All unique responses from survey\n",
        "\n",
        "df['payment_recipient'] = df['payment_to']\n",
        "\n",
        "# Define a function to map the values to the desired categories\n",
        "def map_to_categories(value):\n",
        "    if pd.isna(value) or isinstance(value, float):  # Check for NaN or float values\n",
        "        return np.nan  # Return NaN for missing values\n",
        "    elif value == 'umeme':\n",
        "        return 'Umeme'\n",
        "    elif value == 'landlord':\n",
        "        return 'Landlord'\n",
        "    elif value == 'kamyufu':\n",
        "        return 'Kamyufu'\n",
        "    elif value == 'neighbor':\n",
        "        return 'Neighbor'\n",
        "    elif 'no_one' in value or 'other' in value:\n",
        "        return 'No one/other'\n",
        "    else:\n",
        "        return 'Two bill collectors'\n",
        "\n",
        "# Apply the mapping function to create the new 'payment_category' column\n",
        "df['payment_recipient'] = df['payment_recipient'].apply(map_to_categories)"
      ],
      "metadata": {
        "id": "g5DazXS8fyD8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Functions"
      ],
      "metadata": {
        "id": "ZbiTGMlOwfIE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def interpolate_colors(colors, num_colors):\n",
        "    \"\"\"\n",
        "    Interpolates the given list of colors to generate the specified number of colors.\n",
        "    \"\"\"\n",
        "    cmap = LinearSegmentedColormap.from_list('custom_cmap', colors, N=num_colors)\n",
        "    return [cmap(i) for i in range(cmap.N)]"
      ],
      "metadata": {
        "id": "6WccScm6wfck"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Analysis"
      ],
      "metadata": {
        "id": "w1tNKYJUf5d2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fuel use"
      ],
      "metadata": {
        "id": "hQWuUqQVf8l9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the color categories based on the closest matches\n",
        "color_categories = {\n",
        "    'green': '#8FD14F',  # Green\n",
        "    'light_blue': '#12CDD4',  # Light blue\n",
        "    'gray': '#696969',  # Gray\n",
        "    'red': '#F24726',  # Red\n",
        "    'other': '#414BB2'  # Other\n",
        "}\n",
        "\n",
        "# Define the categories for each energy source\n",
        "energy_categories = {\n",
        "    'Electricity': 'other', 'Phone flashlight': 'other',\n",
        "    'Solar home system': 'other', 'Solar lantern': 'other',\n",
        "    'Charcoal': 'other', 'Firewood': 'other',\n",
        "    'Kerosene lamp': 'other', 'Gas': 'other',\n",
        "    'Candles': 'other', 'Batteries': 'other', 'Car battery': 'other', 'Generator': 'other',\n",
        "    'Energy briquettes': 'other', 'Animal waste': 'other', 'Kerosene stove': 'other',\n",
        "    'Rechargeable lamp': 'other', 'Other': 'other'\n",
        "}\n",
        "\n",
        "# Column labels\n",
        "labels = ['Electricity', 'Charcoal', 'Gas', 'Phone flashlight', 'Candles', 'Solar lantern', 'Solar home system', 'Kerosene lamp', 'Batteries', 'Car battery', 'Generator', 'Energy briquettes', 'Firewood', 'Animal waste', 'Kerosene stove', 'Rechargeable lamp', 'Other']\n",
        "\n",
        "# Corresponding column names in the DataFrame\n",
        "columns = [\n",
        "    'used_energy_source_grid_electricity', 'used_energy_source_charcoal', 'used_energy_source_gas',\n",
        "    'used_energy_source_phone_torch', 'used_energy_source_candles', 'used_energy_source_solar_lantern',\n",
        "    'used_energy_source_shs', 'used_energy_source_kerosene_lamp', 'used_energy_source_batteries',\n",
        "    'used_energy_source_car_battery', 'used_energy_source_generator', 'used_energy_source_energy_briquettes',\n",
        "    'used_energy_source_firewood', 'used_energy_source_animal_waste', 'used_energy_source_kerosene_stove',\n",
        "    'used_energy_source_rechargeable_lamp', 'used_energy_source_other'\n",
        "]\n",
        "\n",
        "# Ensure that only existing columns are used\n",
        "existing_columns = [col for col in columns if col in df.columns]\n",
        "existing_labels = [labels[columns.index(col)] for col in existing_columns]\n",
        "\n",
        "# Calculate the percentage of respondents using each energy source\n",
        "percentages = df[existing_columns].mean() * 100\n",
        "\n",
        "# Find the threshold value for grouping into \"Other\"\n",
        "threshold = 3  # Group into \"Other\" if less than 9% of total responses\n",
        "\n",
        "# Create a DataFrame for plotting\n",
        "plot_data = pd.DataFrame({'Energy Source': existing_labels, 'Percentage': percentages.values})\n",
        "\n",
        "# Group less common sources into \"Other\"\n",
        "other_percentage = plot_data.loc[plot_data['Percentage'] < threshold, 'Percentage'].sum()\n",
        "plot_data = plot_data.loc[plot_data['Percentage'] >= threshold]\n",
        "other_row = pd.DataFrame({'Energy Source': ['Other'], 'Percentage': [other_percentage]})\n",
        "plot_data = pd.concat([plot_data, other_row], ignore_index=True)\n",
        "\n",
        "# Sort the data by percentage in ascending order\n",
        "plot_data = plot_data.sort_values(by='Percentage', ascending=True)\n",
        "\n",
        "# Create a color mapping for the existing labels\n",
        "colors_mapped = [color_categories[energy_categories[label]] for label in plot_data['Energy Source']]\n",
        "\n",
        "# Plotting the horizontal bar chart with color categories and legend at the bottom right\n",
        "fig, ax = plt.subplots(figsize=(7, 8))  # Make the plot square\n",
        "bars = ax.barh(plot_data['Energy Source'], plot_data['Percentage'], color=colors_mapped, edgecolor='black', linewidth=0.5)\n",
        "ax.set_xlabel('Percentage of respondents')\n",
        "ax.set_ylabel('Energy source')\n",
        "ax.set_xlim(0,100)\n",
        "\n",
        "# Format the x-axis as percentages\n",
        "ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'{int(x)}%'))\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "# Save the plot\n",
        "fig.savefig(fig_path + \"Figure 3a.png\", dpi=500)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 783
        },
        "id": "tuApvIYnhW4L",
        "outputId": "a3044d48-d7b5-4bb9-88d1-ea50b527a5a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Appliance ownership"
      ],
      "metadata": {
        "id": "2PHMDAIhSBRo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the color categories based on the provided colors\n",
        "color_categories = {\n",
        "    'Very low': '#12CDD4',  # Light blue\n",
        "    'Low': '#8FD14F',  # Green\n",
        "    'Medium': '#FEF445',  # Yellow\n",
        "    'High': '#FAC710',  # Orange\n",
        "    'Very high': '#F24726',  # Red\n",
        "    'Other': '#414BB2'  # Blue\n",
        "}\n",
        "\n",
        "# Define the categories for each appliance\n",
        "appliance_categories = {\n",
        "    'Lighting': 'Very low', 'Phone charging': 'Very low', 'Radio': 'Very low',\n",
        "    'TV': 'Low', 'Fan': 'Low', 'Refrigerator': 'Medium', 'Iron': 'High',\n",
        "    'Cooking coils': 'High', 'Electric kettle': 'Very high', 'Electric pressure cooker': 'Very high',\n",
        "    'Computer': 'Low', 'Blender': 'Medium', 'Microwave': 'High',\n",
        "    'Printer': 'Low', 'Audio system': 'Low', 'Other': 'Other'\n",
        "}\n",
        "\n",
        "# Column labels\n",
        "appliance_labels = ['Lighting', 'Phone charging', 'Radio', 'TV', 'Fan', 'Refrigerator', 'Iron', 'Cooking coils', 'Electric kettle', 'Electric pressure cooker', 'Computer', 'Blender', 'Microwave', 'Printer', 'Audio system', 'Other']\n",
        "\n",
        "# Corresponding column names in the DataFrame\n",
        "appliance_columns = [\n",
        "    'appliance_ownership_lighting', 'appliance_ownership_phone_charger', 'appliance_ownership_radio',\n",
        "    'appliance_ownership_tv', 'appliance_ownership_fan', 'appliance_ownership_refrigerator',\n",
        "    'appliance_ownership_iron', 'appliance_ownership_coils', 'appliance_ownership_electric_kettle',\n",
        "    'appliance_ownership_epc', 'appliance_ownership_computer', 'appliance_ownership_blender',\n",
        "    'appliance_ownership_microwave', 'appliance_ownership_printer', 'appliance_ownership_audio',\n",
        "    'appliance_ownership_other'\n",
        "]\n",
        "\n",
        "# Ensure that only existing columns are used\n",
        "existing_columns = [col for col in appliance_columns if col in df.columns]\n",
        "existing_labels = [appliance_labels[appliance_columns.index(col)] for col in existing_columns]\n",
        "\n",
        "# Calculate the percentage of respondents owning each appliance\n",
        "appliance_percentages = df[existing_columns].mean() * 100\n",
        "\n",
        "# Create a DataFrame for plotting\n",
        "appliance_plot_data = pd.DataFrame({'Appliance': existing_labels, 'Percentage': appliance_percentages.values})\n",
        "\n",
        "# Sort the data by percentage in ascending order\n",
        "appliance_plot_data = appliance_plot_data.sort_values(by='Percentage', ascending=True)\n",
        "\n",
        "# Create a color mapping for the existing labels\n",
        "colors_mapped = [color_categories[appliance_categories[label]] for label in appliance_plot_data['Appliance']]\n",
        "\n",
        "# Map the power designations to numerical values for plotting and normalization\n",
        "designation_values = {\n",
        "    'Very low': 1, 'Low': 2, 'Medium': 3, 'High': 4, 'Very high': 5, 'Other': 6\n",
        "}\n",
        "numerical_values = [designation_values[des] for des in appliance_designations]\n",
        "\n",
        "# Add numerical values and power designations to the existing DataFrame\n",
        "appliance_plot_data['Power Designation'] = [appliance_categories[label] for label in appliance_plot_data['Appliance']]\n",
        "appliance_plot_data['Numerical Value'] = [designation_values[appliance_categories[label]] for label in appliance_plot_data['Appliance']]"
      ],
      "metadata": {
        "id": "PrRRtL-lSF-h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Ensure the \"Other\" category is explicitly colored gray, without altering the colorbar\n",
        "fig, ax = plt.subplots(figsize=(9, 8))\n",
        "bars = ax.barh(\n",
        "    appliance_plot_data['Appliance'],\n",
        "    appliance_plot_data['Percentage'],\n",
        "    color=[\n",
        "        '#E6E6E6' if val == 6 else cmap(norm(val))  # Color \"Other\" gray, rest follow colormap\n",
        "        for val in appliance_plot_data['Numerical Value']\n",
        "    ],\n",
        "    edgecolor='black',\n",
        "    linewidth=0.5,\n",
        ")\n",
        "ax.set_xlabel('Percentage of respondents')\n",
        "ax.set_ylabel('Appliance')\n",
        "\n",
        "# Set x-axis ticks at 20% intervals and format them as percentages\n",
        "ax.set_xticks(range(0, 101, 25))\n",
        "ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'{int(x)}%'))\n",
        "\n",
        "# Add a color bar to the right of the plot with power designations\n",
        "sm = mpl.cm.ScalarMappable(cmap=cmap, norm=norm)\n",
        "sm.set_array([])\n",
        "cbar = fig.colorbar(sm, ax=ax, orientation='vertical', pad=0.02, ticks=[1, 2, 3, 4, 5])\n",
        "cbar.ax.set_yticklabels(['Very low', 'Low', 'Medium', 'High', 'Very high'])\n",
        "cbar.set_label('Appliance power requirements', fontsize='small')\n",
        "cbar.ax.tick_params(labelsize='small')\n",
        "\n",
        "# Adjust layout to add padding on the right\n",
        "plt.tight_layout(rect=[0, 0, 0.95, 1])  # Adjust rect to give more space on the right\n",
        "\n",
        "# Save the plot\n",
        "fig.savefig(fig_path + \"Figure 3b.png\", dpi=500)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 783
        },
        "id": "dA-AynahRwrh",
        "outputId": "faa62257-106a-4172-d53a-00354821a017"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(appliance_percentages)"
      ],
      "metadata": {
        "id": "V6vj4w6KZE9c",
        "outputId": "0180e8c2-8627-401d-f998-ae752ed74ebb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "appliance_ownership_lighting           97.357724\n",
            "appliance_ownership_phone_charger      94.918699\n",
            "appliance_ownership_radio              50.813008\n",
            "appliance_ownership_tv                 81.707317\n",
            "appliance_ownership_fan                 9.959350\n",
            "appliance_ownership_refrigerator       38.211382\n",
            "appliance_ownership_iron               59.349593\n",
            "appliance_ownership_coils               4.268293\n",
            "appliance_ownership_electric_kettle    31.097561\n",
            "appliance_ownership_epc                 3.455285\n",
            "appliance_ownership_computer            6.910569\n",
            "appliance_ownership_blender             9.552846\n",
            "appliance_ownership_microwave           1.016260\n",
            "appliance_ownership_printer             0.813008\n",
            "appliance_ownership_audio               7.926829\n",
            "appliance_ownership_other               8.130081\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Estimated consumption"
      ],
      "metadata": {
        "id": "Av0UHL1tO2gC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Adding identifier/index to the data frame\n",
        "df[\"id\"] = range(1, len(df) + 1)\n",
        "\n",
        "# Estimate monthly consumption based on electricity payments\n",
        "\n",
        "df[\"total_payment_month\"] = df[\"elec_payment_amount\"] * 1000\n",
        "\n",
        "df[\"total_consumption_month\"] = (df[\"total_payment_month\"] / (exchange_rate*0.22)).round(2)"
      ],
      "metadata": {
        "id": "Kx4abmWPO5IH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "display(df[\"total_consumption_month\"] )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "lbRNgUSjQzr4",
        "outputId": "9e72ea82-589b-405c-af05-79eef74bda51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0        NaN\n",
              "1      24.21\n",
              "2      36.32\n",
              "3        NaN\n",
              "4      24.21\n",
              "       ...  \n",
              "496    24.21\n",
              "497    36.32\n",
              "498    30.26\n",
              "499    12.11\n",
              "500      NaN\n",
              "Name: total_consumption_month, Length: 501, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total_consumption_month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>24.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>36.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>24.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>36.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>30.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>12.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>500</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>501 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter out NaN or inf values\n",
        "df_cleaned = df[df['total_consumption_month'].notna() & np.isfinite(df['total_consumption_month'])]\n",
        "\n",
        "# Sort the data for cumulative distribution\n",
        "consumption_sorted = df_cleaned['total_consumption_month'].sort_values()\n",
        "cumulative_prob = np.linspace(0, 1, len(consumption_sorted))\n",
        "\n",
        "# Remove duplicates by averaging cumulative probabilities for each unique consumption value\n",
        "unique_consumption = consumption_sorted.unique()\n",
        "unique_probabilities = [cumulative_prob[consumption_sorted == val].mean() for val in unique_consumption]\n",
        "\n",
        "# Smooth the curve by interpolating and applying a Gaussian filter\n",
        "x_smooth = np.linspace(min(unique_consumption), max(unique_consumption), 1000)  # Denser range for smoothness\n",
        "spl = make_interp_spline(unique_consumption, unique_probabilities, k=3)  # Cubic spline\n",
        "y_smooth = spl(x_smooth)\n",
        "\n",
        "# Apply a Gaussian filter for additional smoothing\n",
        "y_smooth = gaussian_filter1d(y_smooth, sigma=4)\n",
        "\n",
        "# Calculate the mean consumption\n",
        "mean_consumption = df_cleaned['total_consumption_month'].mean()\n",
        "\n",
        "# Create the plot\n",
        "plt.figure(figsize=(12, 4))\n",
        "plt.fill_between(x_smooth, y_smooth, color=\"blue\", alpha=0.5)\n",
        "plt.axvline(x=mean_consumption, color=\"blue\", linestyle=\"--\", label=f\"Average: {round(mean_consumption)} kWh/month\")\n",
        "\n",
        "# Add thin vertical black lines at specified points\n",
        "special_lines = [0.375, 6.25, 30.4]\n",
        "for x in special_lines:\n",
        "    plt.axvline(x=x, color=\"black\", linestyle=\"-\", linewidth=0.5, zorder=0)\n",
        "\n",
        "plt.xlabel(\"Monthly consumption (kWh per household per month)\", fontsize=14)\n",
        "plt.ylabel(\"Cumulative probability\", fontsize=14)\n",
        "plt.ylim(0, 1)\n",
        "plt.xlim(0, 100)\n",
        "plt.xticks(ticks=range(0, 101, 10))\n",
        "plt.tick_params(axis='both', labelsize=14)\n",
        "plt.legend(frameon=False, fontsize=14)\n",
        "plt.tight_layout()\n",
        "\n",
        "\n",
        "# Save the plot\n",
        "plt.savefig(fig_path + \"Figure 3c.png\", dpi=500)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "bWR31q5cPXGF",
        "outputId": "3de460e7-ebfe-404d-bfec-9f3b841b2b3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Undervoltage"
      ],
      "metadata": {
        "id": "zuQ3-HT-1bYH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df2.drop_duplicates(subset='time')\n",
        "df2['time'] = pd.to_datetime(df2['time'], errors='coerce')\n",
        "df2 = df2.dropna(subset=['time'])\n",
        "\n",
        "def get_time_group(hour):\n",
        "    if 7 <= hour < 16:\n",
        "        return 'Daytime'\n",
        "    elif 16 <= hour < 21:\n",
        "        return 'Evening peak'\n",
        "    else:\n",
        "        return 'Nighttime'\n",
        "\n",
        "df2.loc[:, 'time_group'] = df2['time'].dt.hour.apply(get_time_group)\n",
        "\n",
        "def get_site_type(site_id):\n",
        "    if isinstance(site_id, str):\n",
        "        if site_id.endswith('inf'):\n",
        "            return 'Informal'\n",
        "        elif site_id.endswith('fom'):\n",
        "            return 'Formal'\n",
        "    return 'Unknown'\n",
        "\n",
        "df2.loc[:, 'site_type'] = df2['site_id'].apply(get_site_type)\n",
        "df2.loc[:, 'low_voltage'] = df2['voltage'] < (240 * 0.94)\n",
        "\n",
        "summary = df2.groupby(['site_type', 'time_group'])['low_voltage'].agg(['sum', 'count']).reset_index()\n",
        "summary.rename(columns={'sum': 'low_voltage_count', 'count': 'n'}, inplace=True)\n",
        "summary['p'] = summary['low_voltage_count'] / summary['n']\n",
        "\n",
        "z = 1.96\n",
        "summary['ci'] = z * np.sqrt(summary['p'] * (1 - summary['p']) / summary['n'])\n",
        "summary['percentage'] = summary['p'] * 100\n",
        "summary['ci_pct'] = summary['ci'] * 100\n",
        "\n",
        "pivot_mean = summary.pivot(index='time_group', columns='site_type', values='percentage')\n",
        "pivot_ci = summary.pivot(index='time_group', columns='site_type', values='ci_pct')\n",
        "\n",
        "pivot_mean = pivot_mean.reindex(['Daytime', 'Evening peak', 'Nighttime'])\n",
        "pivot_ci = pivot_ci.reindex(['Daytime', 'Evening peak', 'Nighttime'])\n",
        "pivot_mean = pivot_mean[['Informal', 'Formal']]\n",
        "pivot_ci = pivot_ci[['Informal', 'Formal']]\n",
        "\n",
        "colors = ['#FAC710', '#414BB2']  # Informal, Formal\n",
        "\n",
        "# Font sizes\n",
        "tick_font_size = plt.rcParams['font.size'] - 4\n",
        "label_font_size = tick_font_size + 2\n",
        "legend_font_size = tick_font_size\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(8, 6))\n",
        "\n",
        "pivot_mean.plot(\n",
        "    kind='bar',\n",
        "    ax=ax,\n",
        "    color=colors,\n",
        "    yerr=pivot_ci.values.T,\n",
        "    capsize=4,\n",
        "    edgecolor='black',\n",
        "    linewidth=0.5\n",
        ")\n",
        "\n",
        "ax.set_ylabel('Percentage of hours under voltage (%)', fontsize=label_font_size)\n",
        "ax.set_xlabel('', fontsize=label_font_size)  # Remove x-axis title\n",
        "ax.tick_params(axis='both', labelsize=tick_font_size)\n",
        "\n",
        "# Custom x-axis tick labels without rotation\n",
        "ax.set_xticklabels([\n",
        "    'Daytime (7 am–4 pm)',\n",
        "    'Evening peak (4 pm–9 pm)',\n",
        "    'Nighttime (9 pm–7 am)'\n",
        "], rotation=0)\n",
        "\n",
        "legend = ax.legend(fontsize=legend_font_size)\n",
        "legend.set_frame_on(False)\n",
        "legend.set_title(None)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.title('')\n",
        "plt.show()\n",
        "\n",
        "# Save the plot\n",
        "fig.savefig(fig_path + \"Figure 6.png\", dpi=500)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "jdMM2jjS1dbg",
        "outputId": "ae22112d-8610-4dc7-f083-919e94c05c48"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# In-text citations"
      ],
      "metadata": {
        "id": "8Qb-uIUPh7jw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Meter sharing"
      ],
      "metadata": {
        "id": "dQX_Wfzujocl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter the DataFrame where 'corrected_modality' is equal to 5, 6, or 7\n",
        "filtered_df = df[df['corrected_modality'].isin([5, 6, 7])]\n",
        "\n",
        "# Calculate the percentages\n",
        "percentages = (len(filtered_df)/len(df)) * 100\n",
        "print(percentages)"
      ],
      "metadata": {
        "id": "5O3wyvbQ0lmu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "351a5817-d9a1-4e95-bf65-22382654e9f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "66.7311411992263\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Average users per connection"
      ],
      "metadata": {
        "id": "StRIk8o2jtOf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter the DataFrame where 'corrected_modality' is equal to 5, 6, or 7\n",
        "filtered_df = df[df['corrected_modality'].isin([5, 6, 7])]\n",
        "\n",
        "# Further filter out rows where 'number_Yaka_connections' is equal to 999\n",
        "filtered_df = filtered_df[filtered_df['number_Yaka_connections'] != 999]\n",
        "\n",
        "# Calculate the average of 'number_Yaka_connections' in the filtered DataFrame\n",
        "average_number_Yaka_connections = filtered_df['number_Yaka_connections'].mean()\n",
        "\n",
        "# Display the average\n",
        "print(f\"Average number of Yaka connections: {average_number_Yaka_connections}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bRISS-_Xjstv",
        "outputId": "42486768-5d88-4072-d4af-52f40f57e50e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average number of Yaka connections: 5.175572519083969\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Payment recipient"
      ],
      "metadata": {
        "id": "2YX7a-e-lGHP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter out blank responses in 'payment_to'\n",
        "filtered_df = df[df['payment_to'].notna()]\n",
        "\n",
        "# Calculate the value counts for the 'payment_to' column\n",
        "value_counts = filtered_df['payment_to'].value_counts()\n",
        "\n",
        "# Calculate the percentages\n",
        "percentages = (value_counts / len(filtered_df)) * 100\n",
        "\n",
        "# Display the percentages\n",
        "percentages_df = percentages.reset_index()\n",
        "percentages_df.columns = ['Payment To', 'Percentage']\n",
        "print(percentages_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ql5tpWblGY_",
        "outputId": "0663dd2b-6432-4190-80a3-51ad51af000b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "           Payment To  Percentage\n",
            "0               umeme   47.755102\n",
            "1            landlord   26.122449\n",
            "2             kamyufu   16.122449\n",
            "3            neighbor    6.530612\n",
            "4    landlord kamyufu    0.816327\n",
            "5       umeme kamyufu    0.408163\n",
            "6              no_one    0.408163\n",
            "7   landlord neighbor    0.408163\n",
            "8      umeme landlord    0.408163\n",
            "9               other    0.204082\n",
            "10       umeme no_one    0.204082\n",
            "11    landlord no_one    0.204082\n",
            "12   kamyufu landlord    0.204082\n",
            "13   kamyufu neighbor    0.204082\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Suggested electricity tariff"
      ],
      "metadata": {
        "id": "5Q1Ak0GFpG9B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the average of the 'per_unit_affordable' column, excluding 999 values\n",
        "average_per_unit_affordable = df[df['per_unit_affordable'] != 999]['per_unit_affordable'].mean()\n",
        "\n",
        "# Print the result\n",
        "print(\"Average (excluding 999 values):\", average_per_unit_affordable)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aQi7vcJnpJAE",
        "outputId": "1e996fbf-67fa-4002-ba9a-8980690aae38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average (excluding 999 values): 432.55440414507774\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(average_per_unit_affordable/exchange_rate)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nmJ57NOZptRY",
        "outputId": "16cd6bee-504a-40d5-8135-9d3601bc96df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.11519578267222673\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hours undervoltage"
      ],
      "metadata": {
        "id": "iJBrMiJ85qnv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter for informal and formal sites\n",
        "informal_df = df2[df2['site_type'] == 'Informal']\n",
        "formal_df = df2[df2['site_type'] == 'Formal']\n",
        "\n",
        "# Informal site stats\n",
        "total_informal_hours = len(informal_df)\n",
        "undervoltage_informal = informal_df['low_voltage'].sum()\n",
        "percentage_informal = (undervoltage_informal / total_informal_hours) * 100\n",
        "\n",
        "print(f\"Undervoltage hours in informal sites: {int(undervoltage_informal)} out of {total_informal_hours} total hours\")\n",
        "print(f\"Percentage of hours with undervoltage in informal sites: {percentage_informal:.2f}%\")\n",
        "\n",
        "# Formal site stats\n",
        "total_formal_hours = len(formal_df)\n",
        "undervoltage_formal = formal_df['low_voltage'].sum()\n",
        "percentage_formal = (undervoltage_formal / total_formal_hours) * 100\n",
        "\n",
        "print(f\"\\nUndervoltage hours in formal sites: {int(undervoltage_formal)} out of {total_formal_hours} total hours\")\n",
        "print(f\"Percentage of hours with undervoltage in formal sites: {percentage_formal:.2f}%\")\n"
      ],
      "metadata": {
        "id": "tlHQOb1mp3Kz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1152323-6300-4689-debf-7fe2bb133588"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Undervoltage hours in informal sites: 514 out of 1209 total hours\n",
            "Percentage of hours with undervoltage in informal sites: 42.51%\n",
            "\n",
            "Undervoltage hours in formal sites: 109 out of 563 total hours\n",
            "Percentage of hours with undervoltage in formal sites: 19.36%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PKDVm0Kn5wim"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}